BranchIP learns adaptive tensor-product computation and speeds equivariant MLIPs by up to 2.4x with up to 2.6x less memory on a heterogeneous catalysis system and a proton-conducting solid acid electrolyte while maintaining physical fidelity
The authors present BranchIP (Branch Interatomic Potential), a single-model framework for learned adaptive tensor-product computation trained with a novel distillation loss; in experiments on two systems of physical interest, a heterogeneous catalysis system and a proton-conducting solid acid electrolyte, it accelerates equivariant machine learning interatomic potentials across model sizes by up to 2.4x while reducing memory usage by up to 2.6x, maintains physical fidelity, and uses the learned adaptive computation to reveal which interactions demand deeper computation and how computational depth relates to chemical complexity and dynamics.